A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models

Shaheen, Abdullah M.; Ginidi, Ahmed; El‐Sehiemy, Ragab A.; Ghoneim, Sherif S. M.

doi:10.1109/access.2020.3046536

Cited by 83 publications

(50 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the follower fitness is better than its corresponding leader, the follower became a leader and the leader changed to a follower as in (24). The leader's positions are improved based on random function as in (25), and (26). The best global score and position are identified as in (27).…”

Section: Cbmo Search Algorithm Proceduresmentioning

confidence: 99%

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this paper, a comprehensive methodology based on coot bird metahurestic optimizer called CBMO is presented aiming to demonstrate the performance of solar units under various operating and loading scenarios. The CBMO is capable to deal with different complicated behavior in both harmonized or disorderly phases and can define its major parameters with proofed indicators. It can convey from exploration state to exploitation state to realize a fast and better convergence without trapping in local minima. At first stage of this effort, the optimal values of uncertain parameters of well-known benchmarking PV units under study are cropped with compulsory validations and comparisons. It can be mentioned here both single-and doublediode models of such units are utilized, and their unknown parameters are emphasized. Then, a Simulink detailed model is built-up employing the obtained parameters with various types of connected loads such as static resistive load and dynamic DC motor to appraise the operating performance of such units. It is worth mentioning that the performance of solar units under varied sun radiations and cell temperatures are made along the presented work. To sum up, it can be empathized that the CBMO performs well, and its results are very competitive to those available in the literature.

show abstract

Section: Cbmo Search Algorithm Proceduresmentioning

confidence: 99%

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is no doubt that the accuracy of the behavior of PVs is based on the estimated parameters, so the optimization techniques need further development to achieve high accuracy of these parameters. Additionally, in [43], another optimization method called forensic optimizer was developed for finding the optimal parameters of various solar cells. In [44], the gradient based optimizer was developed for three diode models.…”

Section: Introductionmentioning

confidence: 99%

Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer

et al. 2021

Self Cite

View full text Add to dashboard Cite

Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.

show abstract

“…These structures are influenced by natural events, such as swarming activities, mechanisms focused on nature, and physics. Genetic algorithm (GA) [18], [19], particle swarm optimization [20]- [22], enhanced leader particle swarm optimization algorithm (PSO) [23], niche particle swarm optimization in parallel computing algorithm [24], several versions of differential evolution (DE) [25]- [28], penalty-based DE algorithm [29], sunflower optimizer [30], grey wolf optimizer (GWO) [31], whale optimizer algorithm (WOA) [32], harris-hawk optimizer (HHO) [33], improved salp swarm algorithm (ISSA) [34], several version of JAYA algorithm [35], multiple learning backtracking search algorithm [36], coyote optimization algorithm [37], teaching-learning-based optimization and its various versions [38]- [44], political optimizer (PO) [4], evolutionary shuffled frog leaping algorithm [45], slime-mould optimizer (SMO) [46], [47], marine predator algorithm (MPA) [48], equilibrium optimizer (EO) [49], ions motion optimization (IMO) [50], improved PSO (IPSO) [51], Forensic-based investigation algorithm [52], and improved learning-search algorithm [53] are among good heuristic-based structures. Some studies have endeavored to hybridize a few of these strategies to boost their performance, such as hybrid grey wolf optimizer with cuckoo search algorithm [54], hybrid firefly with pattern search algorithms [55], hybrid grey wolf optimizer with particle swarm algorithm [56], hybrid WO with DE algorithm [26], hybrid GA with simulated annealing algorithm [18], etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

show abstract

A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models

Cited by 83 publications

References 66 publications

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Contact Info

Product

Resources

About